Unlike other media, research on credibility of information present on social media is limited. This limitation is even worse in the case of healthcare, including dementia-related information. The purpose of this study was to identify user groups that show high bot-like behavior and profile features that contribute to high deviation from human behavior. We collected 16,691 tweets about dementia posted over a month by 8400 users. We applied inductive coding to categorize users. The BotOrNot? API was used to compute a bot score. This work provides insight into relations of different user features with a bot score. We performed analysis techniques such as Kruskal-Wallis, stepwise multiple variable regression, user tweet frequency analysis and content analysis on the data. These were further evaluated for the most frequently referenced URLs in the tweets and most active users in terms of tweet frequency. Initial results indicated that the majority of users are regular users and not bots. However, independent variables in the user profiles such as geo_data, and favourites_count were related to the final bot score. Regression analysis showed different features are strongly related. Similarly, content analysis of the tweets showed that the word features of bot profiles have an overall smaller percentage of words compared to regular profiles. Although this analysis is promising, yet it needs further enhancements. First, the observation is performed only on Twitter data as part of developing credibility assessment framework. The results should be validated through user-focused methods. Secondly, the results of different parameters are collected in isolation. A framework should be developed for assessing and diagnosing the credibility of contents posted by users related to dementia in a unified way. This framework could also evaluate the role of tweets in spreading unreliable information.

PurposeThe study aimed to examine how different communities concerned with dementia engage and interact on Twitter.Design/methodology/approachA dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.FindingsClassification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.Originality/valueThe study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.

Since the development of information systems during the last decade, cybersecurity has become a critical concern for many groups, organizations, and institutions. Malware applications are among the commonly used tools and tactics for perpetrating a cyberattack on Android devices, and it is becoming a challenging task to develop novel ways of identifying them. There are various malware detection models available to strengthen the Android operating system against such attacks. These malware detectors categorize the target applications based on the patterns that exist in the features present in the Android applications. As the analytics data continue to grow, they negatively affect the Android defense mechanisms. Since large numbers of unwanted features create a performance bottleneck for the detection mechanism, feature selection techniques are found to be beneficial. This work presents a Rock Hyrax Swarm Optimization with deep learning-based Android malware detection (RHSODL-AMD) model. The technique presented includes finding the Application Programming Interfaces (API) calls and the most significant permissions, which results in effective discrimination between the good ware and malware applications. Therefore, an RHSO based feature subset selection (RHSO-FS) technique is derived to improve the classification results. In addition, the Adamax optimizer with attention recurrent autoencoder (ARAE) model is employed for Android malware detection. The experimental validation of the RHSODL-AMD technique on the Andro-AutoPsy dataset exhibits its promising performance, with a maximum accuracy of 99.05%.

The presence of incorrect, medically uncorroborated information on social media may be harmful if people believe it. The purpose of this qualitative study was to identify how Twitter users evaluate the credibility of dementia-related information sources. It used a think-aloud protocol via semi-structured interviews with 13 caregivers. It identified main credibility dimensions, including 13 factors. Participants deployed a combination of heuristics to assess information sources, and engaged in intensive systematic content review based on prior knowledge and relevance. The findings contribute to a nuanced understanding of how users evaluate Twitter sources in the health domain. Some of these are discussed in light of the MAIN Model, and prove significant in how practitioners and developers can better understand and help users evaluate information.